Study on Multi-Image Registration of Apple Tree at Different Growth Stages

Wei, 周薇 Zhou; Gang, 刘刚 Liu; Xiaodan, 马晓丹 Ma; Juan, 冯娟 Feng

doi:10.3788/aos201434.0215001

Cited by 2 publications

(1 citation statement)

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“…where R is a 3 × 3 orthogonal matrix and T is a translation vector, which was the external parameter determined by the position of the cameras. R and T are solved by using the calibration method provided by Zhou [23], and the final results of R and T are as follows:…”

Section: Calibration Of Multisource Imaging Systemmentioning

confidence: 99%

Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis

Guan

et al. 2018

Remote Sensing

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Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.

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Section: Calibration Of Multisource Imaging Systemmentioning

confidence: 99%